chr8.23607_chr8_78744803_78748050_+_1.R fitVsDatCorrelation=0.818366225789775 cont.fitVsDatCorrelation=0.317242519657754 fstatistic=7997.13764719404,43,485 cont.fstatistic=2929.70328679096,43,485 residuals=-0.609559202523581,-0.0842625062863467,-0.00242555573747481,0.0780109713500732,0.75984147376205 cont.residuals=-0.458913662528839,-0.191609489781088,-0.0447213506947677,0.159670744966264,0.91665087510489 predictedValues: Include Exclude Both chr8.23607_chr8_78744803_78748050_+_1.R.tl.Lung 49.3654095144613 51.0575311866435 68.0352333483236 chr8.23607_chr8_78744803_78748050_+_1.R.tl.cerebhem 61.8037933960887 48.2893403770258 76.2423985428099 chr8.23607_chr8_78744803_78748050_+_1.R.tl.cortex 48.9946013739806 57.9762521452739 85.8876944075754 chr8.23607_chr8_78744803_78748050_+_1.R.tl.heart 51.7592681130172 53.5124807287123 74.2443083231752 chr8.23607_chr8_78744803_78748050_+_1.R.tl.kidney 48.9985148914769 47.94558994867 65.8247608941801 chr8.23607_chr8_78744803_78748050_+_1.R.tl.liver 50.1779185596473 49.2607750701861 59.3955685004909 chr8.23607_chr8_78744803_78748050_+_1.R.tl.stomach 53.5714864148615 61.9399840966357 73.3147649827175 chr8.23607_chr8_78744803_78748050_+_1.R.tl.testicle 51.8611613202749 48.4346440534414 70.5436871203459 diffExp=-1.69212167218215,13.5144530190630,-8.98165077129331,-1.75321261569504,1.05292494280695,0.91714348946116,-8.36849768177427,3.42651726683346 diffExpScore=13.7657451992559 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 63.9926509215854 59.1486697243306 60.6680041435954 cerebhem 59.2336495596977 65.9538452522812 59.5816012381898 cortex 59.2388740139791 60.5191602769468 58.5053914371771 heart 63.127459073579 56.2199966513876 55.7161330606265 kidney 61.8941037064244 61.9894809463326 63.1211775754256 liver 60.8750714815068 63.5379409397657 55.272758736528 stomach 54.2756489806522 70.4890291798283 65.160288210885 testicle 56.6034613326644 52.4105263750628 57.9755971884294 cont.diffExp=4.84398119725478,-6.72019569258352,-1.28028626296769,6.90746242219144,-0.0953772399081458,-2.66286945825892,-16.2133801991761,4.19293495760161 cont.diffExpScore=3.56812852015187 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,-1,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.123882349874516 cont.tran.correlation=-0.360332255547931 tran.covariance=-0.000895946776693356 cont.tran.covariance=-0.00165365403088253 tran.mean=52.1842969493998 cont.tran.mean=60.5943480260015 weightedLogRatios: wLogRatio Lung -0.131984732513578 cerebhem 0.987160656920343 cortex -0.669232119647379 heart -0.132021717876307 kidney 0.0843060446039392 liver 0.0720602090828811 stomach -0.588374585967056 testicle 0.267567648957622 cont.weightedLogRatios: wLogRatio Lung 0.324255937154724 cerebhem -0.444393329652485 cortex -0.0875010252870005 heart 0.473640015666917 kidney -0.00635346727439154 liver -0.176829486531769 stomach -1.07813725297856 testicle 0.307665153160112 varWeightedLogRatios=0.269393997742485 cont.varWeightedLogRatios=0.251979577895759 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.47114727238037 0.0805344609465896 43.1013907783207 5.54263634901454e-168 *** df.mm.trans1 0.444844757778503 0.0644720475811801 6.89980812565904 1.63377931162727e-11 *** df.mm.trans2 0.448581875907632 0.0644720475811801 6.95777306192734 1.12541025721208e-11 *** df.mm.exp2 0.0550805476596652 0.0863324639870368 0.638005046026901 0.523771503907971 df.mm.exp3 -0.113474284571235 0.0863324639870368 -1.31438718797918 0.189337186965043 df.mm.exp4 0.00697998462961693 0.0863324639870368 0.080850056945728 0.935594550208877 df.mm.exp5 -0.0373165760198227 0.0863324639870368 -0.432242684807722 0.665757150961397 df.mm.exp6 0.116306250588416 0.0863324639870368 1.34719021347381 0.178548050870974 df.mm.exp7 0.200243535976421 0.0863324639870368 2.31944655264894 0.0207852925331737 * df.mm.exp8 -0.0396239938662479 0.0863324639870369 -0.458969801582376 0.646461472125633 df.mm.trans1:exp2 0.169634230061018 0.0677247566963765 2.50475953456018 0.0125804455672390 * df.mm.trans2:exp2 -0.110822766909083 0.0677247566963765 -1.63637010031536 0.102410859727752 df.mm.trans1:exp3 0.105934433897915 0.0677247566963765 1.56419068986604 0.118425013801002 df.mm.trans2:exp3 0.240554706077567 0.0677247566963765 3.55194640500551 0.000419737916297162 *** df.mm.trans1:exp4 0.0403735587788901 0.0677247566963765 0.596141806162446 0.551358614670727 df.mm.trans2:exp4 0.0399818672913996 0.0677247566963765 0.59035822706084 0.555225476658475 df.mm.trans1:exp5 0.0298565986610284 0.0677247566963765 0.440852062339352 0.6595165393938 df.mm.trans2:exp5 -0.0255696581769519 0.0677247566963765 -0.377552602980706 0.705928142183326 df.mm.trans1:exp6 -0.0999811566642684 0.0677247566963765 -1.47628668660271 0.140515898606500 df.mm.trans2:exp6 -0.152131183172014 0.0677247566963765 -2.24631568414558 0.0251330143455375 * df.mm.trans1:exp7 -0.118476545990734 0.0677247566963765 -1.74938311734198 0.0808573155448022 . df.mm.trans2:exp7 -0.007030677608885 0.0677247566963765 -0.103812519259463 0.91736104434857 df.mm.trans1:exp8 0.0889442004251877 0.0677247566963765 1.31331886246476 0.189696499291648 df.mm.trans2:exp8 -0.0131137216589598 0.0677247566963765 -0.193632613812866 0.846544626573728 df.mm.trans1:probe2 -0.0528402089973881 0.0463679711801931 -1.13958423567947 0.255022052941981 df.mm.trans1:probe3 -0.0666343155536701 0.0463679711801931 -1.43707636667386 0.151341089878794 df.mm.trans1:probe4 0.00485605131629394 0.0463679711801931 0.104728570017061 0.916634486865035 df.mm.trans1:probe5 -0.0922887336410625 0.0463679711801931 -1.99035522348852 0.0471126767967774 * df.mm.trans1:probe6 -0.0609658088995307 0.0463679711801931 -1.31482588838334 0.189189783700072 df.mm.trans2:probe2 -0.0650216100667775 0.0463679711801931 -1.40229577468666 0.161466664426094 df.mm.trans2:probe3 0.0435297631155537 0.0463679711801931 0.93878947056774 0.348306127924374 df.mm.trans2:probe4 0.109444443579078 0.0463679711801931 2.36034574714861 0.0186528298170320 * df.mm.trans2:probe5 0.198063617949261 0.0463679711801931 4.27156101308714 2.33680842397272e-05 *** df.mm.trans2:probe6 -0.074433636414838 0.0463679711801931 -1.60528128620460 0.109082810976733 df.mm.trans3:probe2 -0.107043506454580 0.0463679711801931 -2.30856567000943 0.0213873460637577 * df.mm.trans3:probe3 -0.0620558628484497 0.0463679711801931 -1.33833465793211 0.181414435634085 df.mm.trans3:probe4 0.022579149645611 0.0463679711801931 0.486955738431275 0.626509693061528 df.mm.trans3:probe5 -0.327713450724120 0.0463679711801931 -7.06766853030027 5.51444181675078e-12 *** df.mm.trans3:probe6 -0.369722430986748 0.0463679711801931 -7.97365986857502 1.11448408193367e-14 *** df.mm.trans3:probe7 -0.248422484427515 0.0463679711801931 -5.35763109975433 1.3053330736854e-07 *** df.mm.trans3:probe8 -0.367464293998194 0.0463679711801931 -7.92495950642677 1.57797157685263e-14 *** df.mm.trans3:probe9 0.0260572532768804 0.0463679711801931 0.561966646666897 0.574398351311115 df.mm.trans3:probe10 -0.0116963495020718 0.0463679711801931 -0.25225062051169 0.800954216054219 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05703767151833 0.132895072465617 30.5281271626378 5.45797233614438e-115 *** df.mm.trans1 0.103640387680501 0.106389455328818 0.974160337226833 0.33046254483974 df.mm.trans2 0.0223724151243827 0.106389455328818 0.210287899822743 0.833531292116257 df.mm.exp2 0.0496922425800725 0.142462728660983 0.348808723847516 0.727384285921402 df.mm.exp3 -0.0179868188233961 0.142462728660983 -0.126256312738465 0.899581358895573 df.mm.exp4 0.0207526981251917 0.142462728660983 0.145671070042303 0.88424158190722 df.mm.exp5 -0.0260726072309879 0.142462728660983 -0.183013532564244 0.854863879362996 df.mm.exp6 0.114774896574905 0.142462728660983 0.805648590713387 0.420840311175932 df.mm.exp7 -0.060723353883121 0.142462728660983 -0.426240283713951 0.670121875096372 df.mm.exp8 -0.198250493312799 0.142462728660983 -1.39159550835625 0.164682823329792 df.mm.trans1:exp2 -0.126970704862315 0.111756958984927 -1.13613242535921 0.256462222592446 df.mm.trans2:exp2 0.0592088401487528 0.111756958984927 0.529800029336324 0.596493022700794 df.mm.trans1:exp3 -0.0592034465651331 0.111756958984927 -0.529751767611339 0.596526462676615 df.mm.trans2:exp3 0.0408927322103433 0.111756958984927 0.365907703482327 0.714593492348618 df.mm.trans1:exp4 -0.0343651030834135 0.111756958984927 -0.307498552175604 0.75859592246723 df.mm.trans2:exp4 -0.0715342925133419 0.111756958984927 -0.640088037139502 0.522417626247823 df.mm.trans1:exp5 -0.00727072017818637 0.111756958984927 -0.0650583215955883 0.948154371373314 df.mm.trans2:exp5 0.0729832154220218 0.111756958984927 0.65305298287389 0.514031565624186 df.mm.trans1:exp6 -0.164719388354692 0.111756958984927 -1.47390721661375 0.141155321503228 df.mm.trans2:exp6 -0.0431917742948541 0.111756958984927 -0.386479505948971 0.699311074760725 df.mm.trans1:exp7 -0.103969220573321 0.111756958984927 -0.930315405122494 0.352670621658672 df.mm.trans2:exp7 0.236126336817756 0.111756958984927 2.11285578063736 0.0351234348684273 * df.mm.trans1:exp8 0.0755523835233968 0.111756958984927 0.676041869872162 0.499336296620825 df.mm.trans2:exp8 0.0773038491815025 0.111756958984927 0.691713964693049 0.48944796202696 df.mm.trans1:probe2 0.0147387214610130 0.0765147592427804 0.192625862080376 0.84733262041847 df.mm.trans1:probe3 0.0337883199739529 0.0765147592427804 0.441592188335103 0.658981150175905 df.mm.trans1:probe4 -0.0544754030736441 0.0765147592427804 -0.711959412964947 0.476832230034153 df.mm.trans1:probe5 0.0623125041338527 0.0765147592427804 0.814385417277939 0.415823868247935 df.mm.trans1:probe6 -0.0869211310196379 0.0765147592427804 -1.13600476404609 0.256515594063464 df.mm.trans2:probe2 0.0128426864323659 0.0765147592427804 0.167845871299369 0.86677449962409 df.mm.trans2:probe3 0.0381333521969857 0.0765147592427804 0.49837903921241 0.618442836545034 df.mm.trans2:probe4 -0.00442621207563342 0.0765147592427804 -0.0578478207268365 0.953893671724681 df.mm.trans2:probe5 -0.0670703293485533 0.0765147592427804 -0.876567214120612 0.381155708170251 df.mm.trans2:probe6 0.0308247213735813 0.0765147592427804 0.402859810037105 0.687228875177838 df.mm.trans3:probe2 -0.147530715308009 0.0765147592427804 -1.92813408508411 0.0544218928670216 . df.mm.trans3:probe3 -0.170263114268199 0.0765147592427804 -2.22523230750757 0.0265247805197835 * df.mm.trans3:probe4 -0.0336203869642978 0.0765147592427804 -0.439397409036088 0.66056931013503 df.mm.trans3:probe5 -0.0745487664992172 0.0765147592427804 -0.974305705683198 0.330390456953801 df.mm.trans3:probe6 -0.159700137522594 0.0765147592427804 -2.08718081456504 0.0373932598100409 * df.mm.trans3:probe7 -0.135691974951074 0.0765147592427804 -1.77340916045394 0.07678840199858 . df.mm.trans3:probe8 0.00346827189777654 0.0765147592427804 0.0453281423361963 0.963864413030091 df.mm.trans3:probe9 -0.0481625398463755 0.0765147592427804 -0.629454242854719 0.529348103412303 df.mm.trans3:probe10 -0.0102909348834051 0.0765147592427804 -0.134496076119799 0.89306610829615